Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Methods of Medium Optimization01:28

Methods of Medium Optimization

19
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
19
Statistical Significance01:37

Statistical Significance

23.9K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
23.9K
The Scientific Method02:40

The Scientific Method

67.1K
Research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.
67.1K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.7K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.7K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

530
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
530
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

394
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
394

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

General scales unlock AI evaluation with explanatory and predictive power.

Nature·2026
Same author

Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites.

Psychometrika·2026
Same author

Servant Leadership in Higher Education: A Graded Response Model Approach to Item Response Theory Analysis.

Psychological reports·2025
Same author

Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites.

Psychometrika·2024
Same author

Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?

The British journal of mathematical and statistical psychology·2023
Same author

Replies to comments on "Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?" by Yuan and Fang (2023).

The British journal of mathematical and statistical psychology·2023
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Mar 26, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K

Local Influence and Robust Procedures for Mediation Analysis.

Jiyun Zu1, Ke-Hai Yuan1

  • 1a University of Notre Dame.

Multivariate Behavioral Research
|January 21, 2016
PubMed
Summary
This summary is machine-generated.

Robust methods improve mediation analysis when data is not normally distributed. These techniques offer more objective and reliable parameter estimates, leading to more accurate conclusions in social and behavioral science research.

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K

Related Experiment Videos

Last Updated: Mar 26, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K

Area of Science:

  • Social and Behavioral Sciences
  • Psychology
  • Statistics

Background:

  • Traditional mediation analysis relies on normal-theory maximum likelihood (ML), which is sensitive to non-normality and outliers.
  • Non-normal data and outliers in social and behavioral sciences can lead to biased parameter estimates and misleading conclusions in mediation studies.

Purpose of the Study:

  • To propose and evaluate robust methods for mediation analysis that address violations of the normality assumption.
  • To compare the performance of robust methods against traditional normal-theory ML in mediation analysis.

Main Methods:

  • Utilizing local influence and robust methods to identify influential cases in mediation analysis.
  • Employing robust methods for parameter estimation and testing mediated effects.
  • Applying proposed methods to simulated and real-world data, including examples on child internalizing problems and ethnic identity mediation.

Main Results:

  • Robust methods provide more objective case influence rankings compared to local influence methods.
  • When normality is violated, robust methods yield estimates with smaller standard errors and more reliable tests of mediated effects than normal-theory ML.
  • An R program is provided to implement these robust procedures for mediation analysis.

Conclusions:

  • Robust methods offer a superior alternative to normal-theory ML for mediation analysis, especially with non-normally distributed data.
  • The proposed robust approaches enhance the accuracy and reliability of mediation effect testing in the social and behavioral sciences.
  • Accessible implementation via an R program facilitates the adoption of these advanced methods.